Can Online Conference Systems Improve Veterinary Education? A Study about the Capability of Online Conferencing and its Acceptance
Bibliographic record
Abstract
In veterinary medicine, there is an ongoing need for students, educators, and veterinarians to exchange the latest knowledge in their respective fields and to learn about unusual cases, emerging diseases, and treatment. Networking among veterinary faculties is developing rapidly, but conferences and meetings can be difficult to attend because of time limitations and travel costs. The current study examines acceptance of synchronous online conferences, seminars, meetings, and lectures by veterinarians and students. First, an online survey on the use of communication technology in veterinary medicine was made available for 15 weeks to every German-speaking veterinary university and via professional journals and an online veterinary forum. A total of 1,776 persons (620 veterinarians and 1,156 students) participated. Most reported using the Internet at least once per day; more than half reported using instant messengers. Most participants used the Internet for communication, but less than half used Skype. Second, to test the spectrum of tools for online conferences, a variety of "virtual classroom" systems (netucate systems iLinc, Adobe Acrobat Connect Pro, Cisco WebEx, Skype) were used to deliver student lectures, veterinary continuing-education courses, and academic conferences at the University of Veterinary Medicine, Hannover (TiHo). Of 591 participants in 63 online events, 99.4% rated the virtual events as enjoyable, 96.1% found them useful, and 92.4% said that they learned a lot. Participants noted that the courses were not tied to a certain place, and thus saved time and travel costs. Online conference systems thus offer new opportunities to provide information in veterinary medicine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".